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update model card README.md

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  1. README.md +10 -7
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  license: apache-2.0
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  base_model: google/vit-base-patch16-224
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  tags:
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- - image-classification
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  - generated_from_trainer
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  datasets:
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  - imagefolder
@@ -15,7 +14,7 @@ model-index:
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  name: Image Classification
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  type: image-classification
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  dataset:
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- name: temp
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  type: imagefolder
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  config: default
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  split: validation
@@ -31,9 +30,9 @@ should probably proofread and complete it, then remove this comment. -->
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  # vit-base
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- This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the temp dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7729
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  - Accuracy: 0.75
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0002
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- - train_batch_size: 16
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  - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 50
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.0 | 50.0 | 100 | 0.7729 | 0.75 |
 
 
 
 
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  ### Framework versions
 
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  license: apache-2.0
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  base_model: google/vit-base-patch16-224
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  tags:
 
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  - generated_from_trainer
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  datasets:
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  - imagefolder
 
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  name: Image Classification
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  type: image-classification
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  dataset:
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+ name: imagefolder
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  type: imagefolder
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  config: default
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  split: validation
 
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  # vit-base
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+ This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.1401
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  - Accuracy: 0.75
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  ## Model description
 
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0002
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+ - train_batch_size: 4
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  - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.1567 | 2.0 | 20 | 0.9874 | 0.75 |
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+ | 0.0007 | 4.0 | 40 | 0.9885 | 0.75 |
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+ | 0.0004 | 6.0 | 60 | 1.1331 | 0.75 |
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+ | 0.0003 | 8.0 | 80 | 1.1414 | 0.75 |
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+ | 0.0003 | 10.0 | 100 | 1.1401 | 0.75 |
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  ### Framework versions